# Prognostics and Health Management

Across 47 public PHM datasets harmonized to one tensor contract, an 801,808-parameter channel-independent Transformer encoder pretrained with masked patch prediction raises fault-classification macro-F1 from 0.350 to 0.812 on the fully withheld CWRU set and from 0.569 to 0.951 on HSG18, with frozen linear probing alone reaching 0.705 and 0.906; the same pretraining does not reliably beat a from-scratch baseline on CMAPSS remaining-useful-life estimation. Simulated federated pretraining over ten clients grouped by physical domain retains part of the representation but trails the centralized encoder at matched budget, and the self-supervised loss does not predict downstream utility.

Across 47 public PHM datasets harmonized to one tensor contract, an 801,808-parameter channel-independent Transformer encoder pretrained with masked patch prediction raises fault-classification macro-F1 from 0.350 to 0.812 on the fully withheld CWRU set and from 0.569 to 0.951 on HSG18, with frozen linear probing alone reaching 0.705 and 0.906; the same pretraining does not reliably beat a from-scratch baseline on CMAPSS remaining-useful-life estimation. Simulated federated pretraining over ten clients grouped by physical domain retains part of the representation but trails the centralized encoder at matched budget, and the self-supervised loss does not predict downstream utility.